IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i9p3211-3225.html
   My bibliography  Save this article

Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models

Author

Listed:
  • Habib Akbari-Alashti
  • Omid Bozorg Haddad
  • Miguel Mariño

Abstract

Forecasting flow in rivers has special significance in surface water management, especially in agricultural planning and risk reduction of floods and droughts. In recent years, studies have shown the superiority of forecasting models based on artificial intelligence, using artificial neural networks (ANN) and genetic programming (GP), over time-series models. In this paper, continuous and discrete historical flow records are used for monthly river flow forecasting of the Saeed-Abad river in East Azarbaijan province, Iran. Auto regressive moving average with exogenous inputs (ARMAX), ANN, and GP models are used in both continuous and discrete flow series. For both flow series, results of the ARMAX, ANN, and GP models are then compared and results of each method are evaluated relative to each other. Two quantitative standard statistical performance evaluation measures, coefficient of determination (R 2 ) and root mean square error (RMSE), are employed to evaluate the performance of the aforementioned models. Results show that for the two methods, the GP model is more effective with respect to accuracy than ARMAX and ANN. For continuous time-series forecasting, GP is a more precise model (R 2 = 0.7 and RMSE = 0.172) than either ANN (R 2 = 0.627 and RMSE = 0.193) or ARMAX (R 2 = 0.595 and RMSE = 0.243). For discrete time-series forecasting, the superiority of the GP model is evident in most months. For monthly flow forecasting, results indicate that the discrete time-series forecasting method is superior to the continuous time-series forecasting method. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
  • Handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3211-3225
    DOI: 10.1007/s11269-015-0991-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-0991-1
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11269-015-0991-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. S. Seifollahi-Aghmiuni & Omid Bozorg Haddad & M. Omid & M. Mariño, 2013. "Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1581-1599, March.
    2. C. Sivapragasam & G. Vasudevan & P. Vincent, 2007. "Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 933-945, June.
    3. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    4. Ashkan Shokri & Omid Bozorg Haddad & Miguel Mariño, 2013. "Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2231-2249, May.
    5. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lan Yu & Soon Keat Tan & Lloyd H. C. Chua, 2017. "Online Ensemble Modeling for Real Time Water Level Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1105-1119, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Application of Fixed Length Gene Genetic Programming (FLGGP) in Hydropower Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3357-3370, July.
    2. Elahe Fallah-Mehdipour & Omid Bozorg Haddad & Saeed Alimohammadi & Hugo Loáiciga, 2015. "Development of Real-Time Conjunctive Use Operation Rules for Aquifer-Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1887-1906, April.
    3. Ali Arefinia & Omid Bozorg-Haddad & Khaled Ahmadaali & Javad Bazrafshan & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8378-8396, June.
    4. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    6. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    7. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    8. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    9. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    10. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    11. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    12. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    13. Suripto & Supriyanto, 2021. "The Effect of the COVID-19 Pandemic on Stock Prices with the Event Window Approach: A Case Study of State Gas Companies, in the Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 155-162.
    14. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    15. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    16. Filippo Beltrami & Fulvio Fontini & Monica Giulietti & Luigi Grossi, 2022. "The Zonal and Seasonal CO2 Marginal Emissions Factors for the Italian Power Market," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(2), pages 381-411, October.
    17. Alireza B. Dariane & M. M. Javadianzadeh & L. Douglas James, 2016. "Developing an Efficient Auto-Calibration Algorithm for HEC-HMS Program," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 1923-1937, April.
    18. Afanasyev, D. & Fedorova, E., 2018. "External and Internal Determinants on the Electricity Market: A Multi-Scale Adaptive Causal Analysis," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 33-54.
    19. Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021. "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, vol. 100(C).
    20. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.

    More about this item

    Keywords

    ARMAX; ANN; GP; Inflow forecasting;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3211-3225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.